GMS location: 1437

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.030 0.346 0.427 2.197 NaN NaN
forest winter 2016 0.988 0.030 0.277 0.384 1.898 0.481 4.052
baseline winter 2017 0.981 0.000e+00 0.567 0.540 3.151 NaN NaN
forest winter 2017 0.981 0.000e+00 0.459 0.489 2.206 0.483 3.716
baseline winter 2018 0.977 0.061 0.281 0.399 1.634 NaN NaN
forest winter 2018 0.977 0.061 0.228 0.369 1.419 0.479 3.349
baseline winter 2019 0.965 0.000e+00 0.305 0.424 1.982 NaN NaN
forest winter 2019 0.983 0.000e+00 0.210 0.354 1.290 0.471 3.104
baseline all 0.981 0.026 0.372 0.445 3.151 NaN NaN
forest all 0.983 0.026 0.293 0.399 2.206 0.479 3.608

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.030 0.346 0.427 2.197 NaN NaN
elr winter 2016 0.982 0.000e+00 0.338 0.450 2.038 0.565 5.315
baseline winter 2017 0.981 0.000e+00 0.567 0.540 3.151 NaN NaN
elr winter 2017 0.981 0.025 0.455 0.480 2.174 0.503 4.540
baseline winter 2018 0.977 0.061 0.281 0.399 1.634 NaN NaN
elr winter 2018 0.985 0.061 0.262 0.404 1.647 0.556 4.670
baseline winter 2019 0.965 0.000e+00 0.305 0.424 1.982 NaN NaN
elr winter 2019 0.983 0.000e+00 0.245 0.404 1.088 0.523 3.719
baseline all 0.981 0.026 0.372 0.445 3.151 NaN NaN
elr all 0.983 0.026 0.327 0.436 2.174 0.540 4.660

Extended logistic regression plots

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